Various methods and systems for analyzing electrocardiogram (ECG) signals are known in the art. For example, U.S. Pat. No. 6,091,990, whose disclosure is incorporated herein by reference, describes a method for plotting symbols representing complexes of selected arrhythmic events on an interactive display screen, for organizing, displaying and interacting with a patient's recorded arrhythmia episodes. A stored arrhythmic episode is selected from a plurality of arrhythmic episodes. A similarity value and a dissimilarity value are calculated for each complex of a plurality of complexes of the selected arrhythmic episode with respect to normal sinus rhythm complexes. Symbols representing the arrhythmic complexes are then plotted as a function of the calculated similarity and dissimilarity values on an interactive display screen.
As another example, U.S. Pat. No. 6,684,100, whose disclosure is incorporated herein by reference, describes a method for curvature-based complex identification and classification. The method includes sensing a cardiac signal and computing curvatures at sample points on the sensed cardiac signal. Features are then extracted from the computed curvatures, and the extracted features are compared with a set of predetermined templates. The sensed cardiac signal is classified based on the outcome of the comparison.
U.S. Pat. No. 5,109,862, whose disclosure is incorporated herein by reference, describes a frequency-domain signal processing and analysis method for ECG signals. Fourier analysis is applied to short overlapping segments of an ECG signal to create a three-dimensional map whose axes are time, frequency and power, thus disclosing changes in the frequency content of the ECG signal over short intervals of time.
U.S. Pat. No. 6,304,773, whose disclosure is incorporated herein by reference, describes a medical device, such as a defibrillator, which automatically detects and reports cardiac asystole. The device obtains ECG data and calculates one or more ECG measures based on the ECG data. The ECG data is classified into classes indicative of cardiac conditions, wherein one class is indicative of cardiac asystole. The defibrillator may classify the ECG data into a rhythm class associated with a cardiac rhythm, such as asystole, and report the rhythm class of the ECG data on the display. Statistical binary classification and regression trees may be used to classify the ECG data according to cardiac rhythm. Other signal data, such as impedance or phonocardiographic signal data may also be obtained and classified with the ECG data.
Other methods for classifying ECG signals are described, for example, by Goletsis et al., in “Automated Ischemic Beat Classification Using Genetic Algorithms and Multicriteria Decision Analysis,” IEEE Transactions on Biomedical Engineering, (51:10), October, 2004, pages 1717-1725, which is incorporated herein by reference.